I'm trying to construct a tree in Python that can take 4 children. The children are defined as an array.
What I'm struggling with is inserting recursively into this tree.
Here is what I've done so far:
Node Class:
class node:
def __init__(self,value,city,children=[None,None,None,None]):
self.value = value
self.children = children
self.city = city
Tree Class:
from node import *
from isFull import *
class tree:
root = None
def __int__(self):
self.root = None
def insert(self, city, value):
if self.root == None:
self.root = node(value, city, children=[None,None,None,None])
else:
self.rec_insert(city, value, self.root, 0)
def rec_insert(self, city, value, nodes, index):
if nodes.children[index] is None:
nodes.children[index] = node(value, city, children=[None,None,None,None])
return
elif index < 3:
self.rec_insert(city, value, nodes, index + 1)
elif index == 3:
self.rec_insert(city, value, nodes.children[0], 0)
So what I have observed is that this first if statement actually works. I can insert a root and into the first layer of children.
if nodes.children[index] is None:
Now the problem arises in level 2. Probably because I'm descending wrong.
At the start I can insert normally into layer 2, however as it gets to the right side of the tree it skips the last child in layer 2.
My logic behind this function:
self.rec_insert(city, value, nodes.children[0], 0)
I wanted to make it descend just into the left most child then my other conditional statements will make it shift right as it inserts.
This check:
elif index == 3:
I use it to determine if all the children have been inserted into in a node.
Any help will be appreciated.
Related
I am trying to write a logic to insert a node into a binary tree.
The node looks like this
class BinTree:
def __init__(self, Id):
self.Id = Id
self.NodeCounter = 1
self.left = None
self.right = None
I need to insert a new node only if it doesnt exist in the tree but increment the counter if it exists already.
As of now, what im doing is whenever i get a new element to insert, i first search it in the binary tree, if the node is found i increment the NodeCounter by 1, otherwise I again start traversing from root node and then go and insert the new node
The problem here is that for every new node, i am traversing the tree twice which i dont want… And when i am trying to search and insert at the same time,the counters get messed because of recursion.
Is there a way I can achieve this?
Any tips would be appreciated
i first search it in the binary tree
...this would only be an efficient process if your binary tree is a binary search tree (BST). I'll assume that is what we are talking about.
if the node is found i increment the NodeCounter by 1, otherwise I again start traversing from root node and then go and insert the new node
Why would you start from the root again? When you did the search and didn't find the node, there was a last node that you visited. Just attach the new node to it.
Here is a possible implementation:
class Node:
def __init__(self, id):
self.id = id
self.nodeCounter = 1
self.left = None
self.right = None
class BinTree:
def __init__(self):
self.root = None
def add(self, id):
self.root = self.addrecur(self.root, id)
def addrecur(self, node, id):
if not node:
node = Node(id)
elif id == node.id:
node.nodeCounter += 1
elif id < node.id:
node.left = self.addrecur(node.left, id)
else:
node.right = self.addrecur(node.right, id)
return node
def __repr__(self):
return self.indented(self.root)
def indented(self, node, indent=""):
if not node:
return ""
return (self.indented(node.right, indent + " ")
+ f"{indent}{node.id} ({node.nodeCounter})\n"
+ self.indented(node.left, indent + " "))
tree = BinTree()
tree.add(4)
tree.add(2)
tree.add(3)
tree.add(2)
tree.add(6)
tree.add(5)
tree.add(5)
print(tree)
This outputs the tree in side-ways view (root at left) with the node count in parentheses:
6 (1)
5 (2)
4 (1)
3 (1)
2 (2)
You will be adding to the classes Node and Tree that we developed in our lectures. There are several short methods that you will have to write.
Write a method is_similar() that takes as input two binary trees and returns true if the nodes have the same key values and are arranged in the same order and false otherwise.
def is_similar (self, pNode):
Write a method print_level() that takes as input the level and prints out all the nodes at that level. If that level does not exist for that binary search tree it prints nothing. Use the convention that the root is at level 1.
def print_level (self, level):
Write a method get_height() that returns the height of a binary tree. Recall that the height of a tree is the longest path length from the root to a leaf.
def get_height (self):
Write a method num_nodes() that returns the number of nodes in the left subtree from the root and the number of nodes in the right subtree from the root and the root itself. This function will be useful to determine if the tree is balanced.
def num_nodes (self):
Input: The input will read from a file. The file will be formatted as follows:
Line 1: Several integers separated by spaces to be inserted into Tree 1
Line 2: Several integers separated by spaces to be inserted into Tree 2
You will read both lines of data. Create two Trees and insert the integers in the order given. Then you will use these two trees to test the methods that you have written.
Output: The output will be formatted as follows:
The Trees are similar: (True or False)
Levels of Tree 1:
print each level on its own line
Levels of Tree 2:
print each level on its own line
Height of Tree 1:
Nodes in Tree 1:
Height of Tree 2:
Nodes in Tree 2:
For example, given the following input file:
14 17 1
14 17 1
This would be the output:
The Trees are similare: True
Levels of Tree 1:
14
1 17
Levels of Tree 2:
14
1 17
Height of Tree 1: 1
Nodes in Tree 1: 3
Height of Tree 2: 1
Nodes in Tree 2: 3
You will be writing helper methods for the Tree class that we developed. The following is the outline of the code that you will be submitting. You may include other functions that we developed for completeness. You may add helper functions as needed.
Below is the code that I need help finishing. Not entirely sure how to start the helper functions or main so any help would be appreciated.
import os
class Node (object):
def __init__ (self, data):
self.data = data
self.lchild = None
self.rchild = None
class Tree (object):
def __init__ (self):
self.root = None
# insert data into the tree
def insert (self, data):
new_node = Node (data)
if (self.root == None):
self.root = new_node
return
else:
current = self.root
parent = self.root
while (current != None):
parent = current
if (data < current.data):
current = current.lchild
else:
current = current.rchild
# found location now insert node
if (data < parent.data):
parent.lchild = new_node
else:
parent.rchild = new_node
# Returns true if two binary trees are similar
def is_similar (self, pNode):
pass
# Prints out all nodes at the given level
def print_level (self, level):
pass
# Returns the height of the tree
def get_height (self):
pass
# Returns the number of nodes in tree which is
# equivalent to 1 + number of nodes in the left
# subtree + number of nodes in the right subtree
def num_nodes (self):
pass
def main():
# write code here
main()
As a hint, think of how you would need to traverse the binary tree in the implementation of each helper method.
For num_nodes, I am not sure what "and the number of nodes in the right subtree from the root and the root itself." means. Should we return the number of nodes in the right subtree + 1?
#classmethod
def count_below(node):
count=0
if (node == None):
return 0 # if one of the root's childs was None
if (node.lchild == None and node.rchild == None): # leaf
return 1 # base case
if (node.lchild != None):
count+=count_below(node.lchild)
if (node.rchild != None):
count+=count_below(node.rchild)
return count
def num_nodes(self):
if (self.root == None):
return 0
return count_below(self.root.lchild), count_below(self.root.rchild) + 1
#classmethod
def depth_below(node):
if node is None:
return 0 # base case
# Compute the depth of each subtree
ldepth = depth_below(node.lchild) # recurse left
rdepth = depth_below(node.rchild) # recurse right
# once all the recursive calls performed on this node's childs resolve
# return the depth of the subtree of this node with the greater depth
if (ldepth > rdepth):
return ldepth+1
else:
return rdepth+1
def get_height(self):
return depth_below(self.root) # depth from root
#classmethod
def explore_childs(node, current_level, target_level):
if (node.lchild==None and node.rchild==None):
return # base case
if (current_level == target_level):
if (node.lchild!=None):
print(node.lchild.data)
if (node.rchild!=None):
print(node.rchild.data)
return # base case
if (node.lchild!=None):
explore_childs(node.lchild, current_level+1, target_level) # recurse left
if (node.rchild!=None):
explore_childs(node.rchild, current_level+1, target_level) # recurse right
def print_level(self, level):
if (level > self.get_height()):
pass # throw error
explore_childs(root, 0, level)
I'm trying to implement a binary tree with insert and preorder methods.
After adding elements to the tree, only one element is displayed.
Can someone let me know where I'm wrong.
Below is code:
class Node(object):
def __init__(self, value, left=None, right=None):
self.value = value
self.left = None
self.right = None
def __repr__(self):
return '{}'.format(self.value)
class BinaryTree(object):
def __init__(self, root=None):
self.root = root
def add(self, value):
val = self.root
if not val:
self.root = value
val = value
elif not val.left:
val = value
elif not val.right:
val = value
else:
self.left = val.left.add(value)
return val
def preorder(self):
val = self.root
if not val: # this will handle the case when root node is None.
return
print(val)
if val.left:
val.left.preorder()
if val.right:
val.right.preorder()
def main():
binary_tree = BinaryTree()
print("Adding nodes to the tree")
for i in range(1, 11):
node = Node(i)
binary_tree.add(node)
print("Printing preorder...")
binary_tree.preorder()
if __name__ == '__main__':
main()
Output
Adding nodes to the tree
Printing preorder...
1
Your code has a few different errors. Some relate to how you modify self.root (or fail to), others have to do with attempts at recursion on the wrong types.
The first issue, which is why your code fails silently, has to do with your BinaryTree.add method, which does nothing when the tree is empty. The problem is that you initialize a local variable val to be equal to your root node (if you have one), and then later rebind it to some other value. But that never changes the root value at all, only the local val variable.
I suggest you get rid of val all together, and instead read and write self.root directly. Then you'll actually make some progress, and see the other issues.
Here's a start:
def add(self, value):
if self.root is None:
self.root = value
elif self.root.left.left is None:
self.root.left = value
...
The other issues I mention are both similar, though one occurs in BinaryTree.add and the other in BinaryTree.preorder. The issue is that you try to call the same method (add or preorder) on one of the children of your root node. But the nodes are Node instances, and don't have the methods that you've defined in the BinaryTree class.
This issue doesn't have as obvious a solution as the previous one. One idea might be to move the logic for the methods into the Node class (where you can recurse easily), and leave only the empty-tree handling code in the BinaryTree methods (everything else gets delegated to the root node).
I have written the program for Binary Search Tree but does not know how can i save it in the Django Database. How can i store it in the models:
from __future__ import print_function
class Node:
# Constructor to initialize data
# If data is not given by user,its taken as None
def __init__(self, data=None, left=None, right=None):
self.data = data
self.left = left
self.right = right
# __str__ returns string equivalent of Object
def __str__(self):
return "Node[Data = %s]" % (self.data,)
class BinarySearchTree:
def __init__(self):
self.root = None
While inserting values in a binary search tree, we first check whether the value is greater than, lesser than or equal to the root of the tree. We initialize current node as the root. If the value is greater than the current node value, then we know that its right location will be in the right subtree. So we make the current element as the right node.
If the value is lesser than the current node value, then we know that its right location will be in the left subtree. So we make the current element as the left node.
If the value is equal to the current node value, then we know that the value is already contained in the tree and doesn't need to be reinserted. So we break from the loop.
def insert(self, val):
if (self.root == None):
self.root = Node(val)
else:
current = self.root
while 1:
if (current.data > val):
if (current.left == None):
current.left = Node(val)
break
else:
current = current.left
elif (current.data < val):
if (current.right == None):
current.right = Node(val)
break
else:
current = current.right
else:
break
In preorder traversal, we first print the current element, then move on to the left subtree and finally to the right subree.
def preorder(self, node):
if (node == None):
return
else:
print(node.data, end=" ")
self.preorder(node.left)
self.preorder(node.right)
In inorder traversal, we first move to the left subtree, then print the current element and finally move to the right subtree.
#Important : Inorder traversal returns the elements in sorted form.
def inorder(self, node):
if (node == None):
return
else:
self.inorder(node.left)
print(node.data, end=" ")
self.inorder(node.right)
In postorder traversal, we first move to the left subtree, then to the right subtree and finally print the current element.
def postorder(self, node):
if (node == None):
return
else:
self.postorder(node.left)
self.postorder(node.right)
print(node.data, end=" ")
tree = BinarySearchTree()
tree.insert(1)
tree.insert(9)
tree.insert(4)
tree.insert(3)
tree.insert(5)
tree.insert(7)
tree.insert(10)
tree.insert(0)
print ("Preorder Printing")
tree.preorder(tree.root)
print("\n\nInorder Printing")
tree.inorder(tree.root)
print("\n\nPostOrder Printing")
tree.postorder(tree.root)
User can add and delete a tree and On adding a tree, user should be prompted to enter at least 3 nodes...
A binary search tree structure should be displayed when a tree is selected, with its
respective nodes in the order of insertion.....
How can all the changes that have been done by the user can be reflected back into the database..
I have the task to perform some basic operations on Binary Search Trees and I'm not sure what is the clever way to do it.
I know that the usual way would be to write a class for the nodes and one for the tree so that I can build up my tree from given values and perform certain tasks on it. The thing is, I'm already getting the tree as a list and since BSTs are not unique, there won't come any good from it if I take each value and build the tree myself.
So... I'm getting a list like this:
11 9 2 13 _, 4 18 2 14 _, 2 10 _ 11 4, 14 16 4 _ _, 13 0 11 _ _ | 10 | 7
which means:
key value parent left right, ... | value1 | value2
So as you see the BST is given explicitly. My tasks are to do a level-print of the tree, return the path from root to value1, do a rotate-right operation on the subtree that has value1, then delete value1 and then insert value2.
What would be an efficient way to tackle this problem?
Here is one possible way of implementing the tree. Hope it helps. Though this contains insertions and popular traversals, not rotations or deletions.
Reference: http://www.thelearningpoint.net/computer-science/learning-python-programming-and-data-structures/learning-python-programming-and-data-structures--tutorial-20--graphs-breadth-and-depth-first-search-bfsdfs-dijkstra-algorithm-topological-search
'''
Binary Search Tree is a binary tree(that is every node has two branches),
in which the values contained in the left subtree is always less than the
root of that subtree, and the values contained in the right subtree is
always greater than the value of the root of the right subtree.
For more information about binary search trees, refer to :
http://en.wikipedia.org/wiki/Binary_search_tree
'''
#Only for use in Python 2.6.0a2 and later
from __future__ import print_function
class Node:
# Constructor to initialize data
# If data is not given by user,its taken as None
def __init__(self, data=None, left=None, right=None):
self.data = data
self.left = left
self.right = right
# __str__ returns string equivalent of Object
def __str__(self):
return "Node[Data = %s]" % (self.data,)
class BinarySearchTree:
def __init__(self):
self.root = None
'''
While inserting values in a binary search tree, we first check
whether the value is greater than, lesser than or equal to the
root of the tree.
We initialize current node as the root.
If the value is greater than the current node value, then we know that
its right location will be in the right subtree. So we make the current
element as the right node.
If the value is lesser than the current node value, then we know that
its right location will be in the left subtree. So we make the current
element as the left node.
If the value is equal to the current node value, then we know that the
value is already contained in the tree and doesn't need to be reinserted.
So we break from the loop.
'''
def insert(self, val):
if (self.root == None):
self.root = Node(val)
else:
current = self.root
while 1:
if (current.data > val):
if (current.left == None):
current.left = Node(val)
break
else:
current = current.left
elif (current.data < val):
if (current.right == None):
current.right = Node(val)
break
else:
current = current.right
else:
break
'''
In preorder traversal, we first print the current element, then
move on to the left subtree and finally to the right subree.
'''
def preorder(self, node):
if (node == None):
return
else:
print(node.data, end=" ")
self.preorder(node.left)
self.preorder(node.right)
'''
In inorder traversal, we first move to the left subtree, then print
the current element and finally move to the right subtree.
'''
#Important : Inorder traversal returns the elements in sorted form.
def inorder(self, node):
if (node == None):
return
else:
self.inorder(node.left)
print(node.data, end=" ")
self.inorder(node.right)
'''
In postorder traversal, we first move to the left subtree, then to the
right subtree and finally print the current element.
'''
def postorder(self, node):
if (node == None):
return
else:
self.postorder(node.left)
self.postorder(node.right)
print(node.data, end=" ")
tree = BinarySearchTree()
tree.insert(1)
tree.insert(9)
tree.insert(4)
tree.insert(3)
tree.insert(5)
tree.insert(7)
tree.insert(10)
tree.insert(0)
print ("Preorder Printing")
tree.preorder(tree.root)
print("\n\nInorder Printing")
tree.inorder(tree.root)
print("\n\nPostOrder Printing")
tree.postorder(tree.root)
Here is the implementation of Binary Search Tree with it's basic operations like insert node, find node
class Node:
def __init__(self,data):
self.left = None
self.right = None
self.data = data
class BST:
def __init__(self):
self.root = None
def set_root(self,data):
self.root = Node(data)
def insert_node(self,data):
if self.root is None:
self.set_root(data)
else:
n = Node(data)
troot = self.root
while troot:
if data < troot.data:
if troot.left:
troot = troot.left
else:
troot.left = n
break
else:
if troot.right:
troot = troot.right
else:
troot.right = n
break
def search_node(self,data):
if self.root is None:
return "Not found"
else:
troot = self.root
while troot:
if data < troot.data:
if troot.left:
troot = troot.left
if troot.data == data:
return "Found"
else:
return "Not found"
elif data > troot.data:
if troot.right:
troot = troot.right
if troot.data == data:
return "Found"
else:
return "Not found"
else:
return "Found"
tree = BST()
tree.insert_node(10)
tree.insert_node(5)
tree.insert_node(20)
tree.insert_node(7)
print(tree.root.data)
print(tree.root.left.data)
print(tree.root.right.data)
print(tree.root.left.right.data)
print(tree.search_node(10))
print(tree.search_node(5))
print(tree.search_node(20))
print(tree.search_node(7))
print(tree.search_node(12))
print(tree.search_node(15))
Output:
10
5
20
7
Found
Found
Found
Found
Not found
Not found
In this specific case I had success using a dictionary as a datatype to store the graph. The key is the node_key and the value is a list with the attributes of the node. In this way it is rather fast to find the needed nodes and all its attributes.
I'm just not sure if there is a way to make it reasonably faster.